{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'font_size_title': 32, 'font_size_axes': 25, 'font_size_ticks': 22, 'font_size_legend': 22, 'line_width': 3, 'marker_size': 10, 'dpi': 600}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "%matplotlib inline\n",
    "import utils\n",
    "import sys, importlib\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('font',**{'family':'serif','serif':['Palatino']})\n",
    "rc('text', usetex=True)\n",
    "\n",
    "importlib.reload(sys.modules['utils'])\n",
    "get_sequences = utils.get_sequences\n",
    "convert_bagsize_key = utils.convert_bagsize_key\n",
    "parse_json = utils.parse_json\n",
    "get_plot_defaults = utils.get_plot_defaults\n",
    "make_like_colab = utils.make_like_colab\n",
    "    \n",
    "out = 'plots_examples'\n",
    "if not os.path.isdir(out):\n",
    "    os.makedirs(out)\n",
    "\n",
    "defaults = get_plot_defaults()\n",
    "print(defaults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rootsift-upright-2k': 'Upright RootSIFT'}\n",
      "dict_keys(['Upright RootSIFT, DEGENSAC w/ th=0.1', 'Upright RootSIFT, DEGENSAC w/ th=0.25', 'Upright RootSIFT, DEGENSAC w/ th=0.5', 'Upright RootSIFT, DEGENSAC w/ th=0.75', 'Upright RootSIFT, DEGENSAC w/ th=1', 'Upright RootSIFT, DEGENSAC w/ th=1.5', 'Upright RootSIFT, DEGENSAC w/ th=2', 'Upright RootSIFT, DEGENSAC w/ th=5'])\n"
     ]
    }
   ],
   "source": [
    "methods, results = {}, {}\n",
    "\n",
    "ths = [0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5, 3, 4, 5, 7.5, 10, 15, 20]\n",
    "\n",
    "methods_cur = {}\n",
    "methods_cur['rootsift-upright-2k'] = 'Upright RootSIFT'\n",
    "for desc_key, desc_name in methods_cur.items():\n",
    "    for th in ths:\n",
    "        key = '{}, DEGENSAC w/ th={}'.format(desc_name, th)\n",
    "        try:\n",
    "            results[key] = parse_json(\n",
    "                os.path.join('..', 'example', 'results', 'val',\n",
    "                    '{}-degensac-inlier-th-{}.json'.format(desc_key, th)))\n",
    "        except Exception:\n",
    "            pass\n",
    "methods.update(methods_cur)\n",
    "\n",
    "print(methods)\n",
    "print(results.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEGENSAC, Upright RootSIFT -> Best mAP=0.5756 at th=0.5\n",
      "\n",
      "Found: 1/1\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 1, figsize=(10,12))\n",
    "cmap = matplotlib.cm.get_cmap('tab20')\n",
    "\n",
    "# config\n",
    "labels = []\n",
    "t = 10\n",
    "ax = axes\n",
    "sac = 'DEGENSAC'\n",
    "\n",
    "count = 0\n",
    "for i, method in enumerate(sorted(methods.values())):\n",
    "    if 'DSP'  in method:\n",
    "        continue\n",
    "    x, y = [], []\n",
    "    for th in ths:\n",
    "        key = '{}, DEGENSAC w/ th={}'.format(method, th)\n",
    "        if key in results:\n",
    "            x.append(th)\n",
    "            y.append(results[key]['phototourism']['results']['allseq']['stereo']['run_avg']['qt_auc_10_th_0.1']['mean'])\n",
    "    if len(y) > 0:\n",
    "        cur_label = method\n",
    "        if cur_label not in labels:\n",
    "            labels.append(cur_label)\n",
    "        ax.plot(\n",
    "            np.log(x),\n",
    "            y,\n",
    "            linestyle='-',\n",
    "            color=cmap(i),\n",
    "            marker=None,\n",
    "            linewidth=defaults['line_width']\n",
    "        )\n",
    "        # plot max\n",
    "        ax.plot(\n",
    "            np.log(x[np.argmax(y)]),\n",
    "            max(y),\n",
    "            linestyle=None,\n",
    "            color=cmap(i),\n",
    "            marker='D',\n",
    "            markersize=7,\n",
    "            label='_nolegend_',\n",
    "        )\n",
    "        count += 1\n",
    "\n",
    "        # choose best, but check if there's a maxima already (run might not have finished)\n",
    "        if x[np.argmax(y)] != min(x) and x[np.argmax(y)] != max(x):\n",
    "            print('{}, {} -> Best mAP={:.04f} at th={}'.format(sac, method, max(y), x[np.argmax(y)]))\n",
    "        else:\n",
    "            print('{}, {} entries, {} -> No maxima yet ({} entries finished)'.format(sac, len(x), method, len(x)))\n",
    "    else:\n",
    "        print('{}, {} -> No data points yet'.format(sac, method))\n",
    "print()\n",
    "print('Found: {}/{}'.format(count, len(methods)))\n",
    "print()\n",
    "\n",
    "# ax.set_title('({}) {}'.format(['a', 'b', 'c', 'd'][cur], sac.replace('NODEGENSAC',\n",
    "#                                                                     'PyRANSAC')), fontsize=defaults['font_size_title'] - 8)\n",
    "ax.set_ylabel('mean Average Accuracy', fontsize=defaults['font_size_axes'])\n",
    "ax.set_xticks(np.log(ths))\n",
    "ax.set_xlabel('Inlier threshold $\\eta$', fontsize=defaults['font_size_axes'])\n",
    "ths_xlabel = [str(v) if v not in [1.25, 1.75, 2.5] else '' for v in ths]\n",
    "ax.set_xticklabels(ths_xlabel, rotation=60)\n",
    "ax.tick_params(axis='both', which='major', labelsize=defaults['font_size_ticks'])\n",
    "ax.set_ylim(0, .82)\n",
    "ax.set_xlim(np.log(0.08), np.log(22))\n",
    "make_like_colab(fig, ax)\n",
    "\n",
    "from pylab import gcf\n",
    "gcf().suptitle('STEREO: mAP(10$^o$) vs Inlier Threshold', fontsize=defaults['font_size_title'])\n",
    "fig.legend(labels=labels, prop={'size': defaults['font_size_legend'] - 3}, loc='lower center', ncol=1)\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(top=0.92, bottom=0.15)\n",
    "\n",
    "# plt.savefig('{}/ransac-th-example.png'.format(out), bbox_inches='tight', dpi=defaults['dpi'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rootsift-upright-2k': 'Upright RootSIFT'}\n",
      "dict_keys(['Upright RootSIFT, RT=0.6', 'Upright RootSIFT, RT=0.65', 'Upright RootSIFT, RT=0.7', 'Upright RootSIFT, RT=0.75', 'Upright RootSIFT, RT=0.8', 'Upright RootSIFT, RT=0.85', 'Upright RootSIFT, RT=0.9', 'Upright RootSIFT, RT=0.95', 'Upright RootSIFT, RT=1'])\n"
     ]
    }
   ],
   "source": [
    "methods, results = {}, {}\n",
    "rts = [0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1]\n",
    "\n",
    "methods_cur = {}\n",
    "methods_cur['rootsift-upright-2k'] = 'Upright RootSIFT'\n",
    "for desc_key, desc_name in methods_cur.items():\n",
    "    for rt in rts:\n",
    "        key = '{}, {}'.format(desc_name, 'RT={}'.format(rt) if rt < 100 else 'No RT')\n",
    "        try:\n",
    "            results[key] = parse_json(\n",
    "                os.path.join('..', 'example', 'results', 'val',\n",
    "                    '{}-degensac-th-0.5-rt-{}.json'.format(desc_key, rt)))\n",
    "        except Exception:\n",
    "            pass\n",
    "methods.update(methods_cur)\n",
    "\n",
    "print(methods)\n",
    "print(results.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STEREO: Upright RootSIFT -> Best mAP=0.5813 at RT=0.9\n",
      "\n",
      "Found: 1/1\n",
      "\n",
      "MULTIVIEW: Upright RootSIFT -> Best mAP=0.6367 at RT=0.9\n",
      "\n",
      "Found: 1/1\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x792 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "method_best_rt_stereo = {}\n",
    "method_best_rt_multiview = {}\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16,11))\n",
    "cmap = matplotlib.cm.get_cmap('tab20')\n",
    "\n",
    "# config\n",
    "labels = []\n",
    "for cur in range(2):\n",
    "    ax = axes[cur]\n",
    "    count  = 0\n",
    "    for i, method in enumerate(sorted(methods.values())):\n",
    "        x, y = [], []\n",
    "        for rt in rts:\n",
    "            key = '{}, {}'.format(method, 'RT={}'.format(rt) if rt < 100 else 'No RT')\n",
    "            if key in results:\n",
    "                if cur == 0:\n",
    "                    x.append(rt)\n",
    "                    y.append(results[key]['phototourism']['results']['allseq']['stereo']['run_avg']['qt_auc_{:02d}_th_0.1'.format(t)]['mean'])\n",
    "                else:\n",
    "                    x.append(rt)\n",
    "                    y.append(results[key]['phototourism']['results']['allseq']['multiview']['run_avg']['bag_avg']['qt_auc_colmap_10']['mean'])\n",
    "        if len(y) > 0:\n",
    "            if method not in labels:\n",
    "                labels.append(method)\n",
    "            ax.plot(\n",
    "                x,\n",
    "                y,\n",
    "                linestyle='-',\n",
    "                color=cmap(i),\n",
    "                marker='.',\n",
    "                markersize=12,\n",
    "                linewidth=defaults['line_width'],\n",
    "            )\n",
    "            # plot max\n",
    "            ax.plot(\n",
    "                x[np.argmax(y)],\n",
    "                max(y),\n",
    "                linestyle=None,\n",
    "                color=cmap(i),\n",
    "                marker='D',\n",
    "                markersize=10,\n",
    "                label='_nolegend_',\n",
    "            )\n",
    "            count += 1\n",
    "            \n",
    "        if len(y) >= 6:\n",
    "            print('{}: {} -> Best mAP={:.04f} at RT={}'.format('STEREO' if cur == 0 else 'MULTIVIEW', method, max(y), x[np.argmax(y)]))\n",
    "            if cur == 0:\n",
    "                method_best_rt_stereo[method] = x[np.argmax(y)]\n",
    "            else:\n",
    "                method_best_rt_multiview[method] = x[np.argmax(y)]\n",
    "        else:\n",
    "            print('{}, {} -> No maxima yet ({}/9 entries finished)'.format('STEREO' if cur == 0 else 'MULTIVIEW', method, len(y)))\n",
    "    print()\n",
    "    print('Found: {}/{}'.format(count, len(methods)))\n",
    "    print()\n",
    "\n",
    "    ax.set_title('{}: mAP({}$^o)$'.format('STEREO' if cur == 0 else 'MULTIVIEW', t), fontsize=defaults['font_size_title'])\n",
    "    ax.set_xlabel('Ratio test $r$', fontsize=defaults['font_size_axes'])\n",
    "    if cur == 0:\n",
    "        ax.set_ylabel('Mean Average Precision (mAP)', fontsize=defaults['font_size_axes'])\n",
    "    else:\n",
    "        ax.set_yticklabels([])\n",
    "    ax.set_xticks(rts)\n",
    "    ax.set_xticklabels([rt if rt < 100 else 'None' for rt in rts])\n",
    "    ax.tick_params(axis='both', which='major', labelsize=defaults['font_size_ticks'])\n",
    "    ax.set_ylim(0, .85)\n",
    "    ax.set_xlim(0.58, 1.02)\n",
    "    make_like_colab(fig, ax)\n",
    "\n",
    "fig.legend(labels, prop={'size': defaults['font_size_legend'] - 4}, loc='lower center', bbox_to_anchor=(0.50, 0), ncol=4, columnspacing=.7)\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(bottom=0.15)\n",
    "\n",
    "# plt.savefig('{}/ransac-rt-example.png'.format(out), bbox_inches='tight', dpi=defaults['dpi'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
